ggplot( top20_breaches_2024, aes(x = records_lost, y = reorder(organisation, records_lost)) ) + geom_point(size = 5, aes(color = sector)) + # label for Ticketmaster geom_text_repel( data = top_lab, aes(label = label_text), direction = "x", hjust = 0, min.segment.length = Inf, segment.size = 0, box.padding = 0.1, point.padding = 0.2, seed = 123 ) + # use Okabe–Ito (color-blind friendly) scale_color_manual(values = okabe_ito) + # force the axis you want, keep extra room for the label scale_x_continuous( trans = "log10", breaks = breaks_vec, labels = labels_vec, expand = expansion(mult = c(0.02, 0.28)) ) + coord_cartesian(clip = "off") + labs( title = "Figure 2. Principales filtraciones de datos de 2024", x = "Records Lost", y = NULL, color = "Sector", caption = "Source: McCandless, D. (2022). Los mayores casos de filtración y piratería informática del mun" ) + # ---- apply your global figure styling ---- theme( plot.title = element_text(size = rel(1.2)), axis.title = element_text(size = rel(1.0)), legend.position = "right", # match Fig. 1 caption alignment & spacing control plot.caption.position = "plot", plot.caption = element_text(hjust = 1, margin = margin(t = 20)), # optional: lighten grid to match your fig 1 feel panel.grid.major.y = element_line(linetype = "dashed", color = "gray80"), panel.grid.major.x = element_blank(), panel.grid.minor.x = element_blank() )

Amanda Buyan

Bio

Amanda is a data analyst for EcoCommons, and is based at the Atlas of Living Australia. She completed her PhD in Structural Biology, and uses her extensive Python skills to manage the integration of data within EcoCommons. She also works to optimize the available scientific workflows within the platform.

Posts

Download a species list and cross-reference with conservation status lists
Knowing what species have been observed in a local area is an important, regular task for ecosystem management and environmental impact assessment. Here we show how to make a species list with {galah-python} and how to cross-reference this list with threatened and sensitive species lists. We also show how to visualise this information as a waffle chart using {pywaffle} & {matplotlib}.

Beginner’s guide to making a quick map of species occurrences in Python and R
The ability to make a map quickly is an essential skill in ecology and conservation. This post shows how to make a quick, simple map of Peron’s tree frog occurrences & set a custom font using either Python or R.

Plotting invasive species distributions with alpha shapes and choropleth maps in Python
Invasive and introduced species can expand quickly into new habitats, altering ecosystems. In this post we use Python’s {galah}, {alphashape} and {GeoPandas} packages to visualise the growing distribution of Rhinella marina (cane toads) and the expanding range of Pittisporum undulatum in Australia.